Chapter 1. A forward-looking assessment of social protection needs

Thanks to a prolonged period of robust economic growth, combined with political and social stability, Indonesia has achieved a dramatic reduction in poverty and major improvements in living standards for the majority of its population over the past two decades. However, there remain structural barriers to the inclusive growth that the government has identified as key to its economic ambitions and sustained social cohesion – barriers that social protection can overcome. This chapter examines the context for social protection, assessing the trajectories of poverty and inequality and analysing the risks individuals face along the life cycle, including pervasive informality. Finally, it maps the threats and opportunities that lie in store for Indonesia, its population and its economy in the future.

    

Indonesia has achieved great political and economic progress since the watershed of the Asian Financial Crisis in 1997-98. Having achieved average annual growth in gross domestic product (GDP) of 5.3% since 2000, it has set its sights on becoming an upper middle income country and one the world’s top ten economies by 2030 (Ministry of Industry, n.d.[1]).

However, Indonesia’s ambitions face significant obstacles which, if not addressed, threaten to leave it in a middle-income trap (World Bank, 2014[2]). Chief among these is the failure of Indonesia’s economic development to benefit the whole population, leaving large groups in poverty and vulnerability, without the skills or opportunities to contribute to economic progress.

Achieving more inclusive growth is critical for ensuring that prosperity is shared, yet Indonesia represents an extraordinary challenge to this objective. It is the world’s fourth most populous country, with a population approaching 270 million in 2018, and consists of 17 000 islands. An estimated 700 languages are spoken by some 300 ethnic groups.

The Government of Indonesia (GoI) has placed social protection at the heart of its inclusive growth strategy. As such, social protection holds the key to Indonesia’s economic future if it can address unevenness in the country’s growth trajectory identified in this chapter.

Indonesia confronts a last-mile problem in reducing poverty

Indonesia’s robust economic performance over the past two decades has driven significant progress in reducing poverty. However, the decline in poverty stalled after 2010, with a large proportion of the population remaining poor or vulnerable. At the same time, inequality has risen. This section examines the dynamics and determinants of poverty and vulnerability, at both an aggregate and individual level.

Figure 1.1. The decline in national poverty has slowed
Poverty headcount ratios (2001-18)
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Source: Statistics Indonesia (2019[3]), Poverty and Inequality: Basic Statistics, bps.go.id/subject/23/kemiskinan-dan-ketimpangan.html#subjekViewTab3; Statistics Indonesia (2016[4]), SUSENAS, microdata.bps.go.id/mikrodata/index.php/catalog/769; World Bank (2018[5]), World Development Indicators, data.worldbank.org/products/wdi.

Indonesia’s national poverty rate fell from 24.2% in 1998 (at the height of the Asian Financial Crisis) to 9.66% in 2018 (Statistics Indonesia, 2019[3]). However, the decline has slowed significantly since 2010 and bringing the poverty rate below 10% has taken longer than expected despite robust economic growth over this period. The National Medium Term Development Plans (Rencana Pembangunan Jangka Menengah Nasional, RPJMN) for the period 2004-09 envisaged the national poverty rate falling to 8.2% and its successor for 2010-14 targeted a decline to 8% (BAPPENAS, 2014[6]).

In a context where economic growth alone has proven unable to eliminate poverty, the GoI recognises social protection as a direct means of reducing poverty, preventing vulnerable households from falling into poverty and reducing inequality both in the short term (though fiscal redistribution) and the long term (by ensuring equality of opportunity for younger generations through access to mechanisms for enhancing human capital).

Box 1.1. National and international poverty measures and terms used in this Review

Monetary or income poverty: Poverty status based on either household consumption or household income as a welfare metric. The national statistical office, Badan Pusat Statistik (BPS) of the Government of Indonesia (GoI), has defined poverty as the “economic inability to fulfil basic food and non-food needs measured by expenditure” (Statistics Indonesia, 2018[7]).

  • National (monetary) poverty line (national; GoI): the minimum amount needed to afford a specific basket of food and non-food basic needs, defined by the food poverty line and the non-food poverty line, respectively. BPS calculates the poverty line twice a year (in March and September), as well as separately for urban and rural areas. Data from the Consumption and Expenditure Module of the nationally representative socio-economic survey, the Survei Sosial Ekonomi Nasional (SUSENAS), are used to assess average expenditure and the appropriate consumption threshold for poverty. In September 2016, the rural poverty line was set at IDR 350 420 per month per capita (USD 27.00 [United States dollars] in current prices), and the urban poverty line at IDR 372 114 per month per capita (USD 28.86 in current prices). In September 2018, the rural poverty line was set at IDR 392 154 per month per capita (USD 27.15 in current prices), and the urban poverty line at IDR 425 770 per month per capita (USD 29.48 in current prices). BPS also calculates provincial poverty lines.

  • Extreme poverty line (national): This threshold is set at 0.8 times the poverty line.

  • Vulnerability line (national): The vulnerability line is equal to 1.5 times the national poverty line.

  • Food poverty line (national; GoI): The food poverty line is set at the amount necessary to afford a basket of food equivalent to 2 100 kilocalories per capita per day. The food basket comprises 52 types of commodities (grains, tubers, fish, meat, eggs and milk, vegetables, nuts, fruits, oils and fats, etc.). The September 2016 national monthly food poverty line was equivalent to about USD 20 per capita in both urban and rural areas (current USD). The September 2018 national monthly food poverty line was equivalent to about USD 21 per capita in both urban and rural areas (current USD).

  • Global poverty lines (international): Poverty lines defined by the World Bank based on the 15 poorest countries in 2005. The International USD 1.90 per day per capita poverty line currently acts as the baseline for action on Sustainable Development Goal (SDG) 1, to end poverty in all its forms everywhere. As of September 2015, the Intl. USD 3.10 lower and middle-income poverty line and Intl. USD 5.20 upper and middle-income poverty line have been updated to reflect changes in inflation and purchasing power parity (PPP), and are now set at Intl. USD 3.20 and Intl. USD 5.50 per day per capita, respectively. The Intl. USD 1.90 lower income poverty line remains unchanged.

  • Poverty rate (GoI): The proportion of the population that lives below a specified poverty line. Based on the 2016 SUSENAS, the national poverty rate in September 2016 was measured at 10.70%. In September 2018, BPS estimated the national poverty rate at 9.66%.

  • Poverty gap: How far (on average) poor households are below the poverty line, expressed as a percentage of the level at which the poverty line is set.

Multi-dimensional or non-income poverty: Poverty status based on deprivations of a household in areas beyond monetary poverty.

  • The Oxford Poverty and Human Development Initiative (OPHI) Multidimensional Poverty Index (MPI) covers three areas: education, health and standard of living through ten indicators: years of schooling, school attendance, child mortality, nutrition, electricity, sanitation, access to or cleanliness of water, conditions of the floor of their shelter, type of cooking fuel used, and assets. The indicators are weighted to create a deprivation score: a score of 33.3% indicates multi-dimensional poverty, a score of 50% or more indicates severe multi-dimensional poverty, and a score between 20-33.3% indicates near multi-dimensional poverty.

  • The United Nations Development Programme (UNDP) Human Development Index (HDI) covers three areas: health, education and standard of living. It is a summary measure based on the geometric mean of normalised indices for each dimension (life expectancy at birth, average and expected years of schooling and gross national income per capita).

The poverty rate calculated according to the World Bank’s International USD 1.90/day poverty line [International United States dollar]) has fallen consistently since 2006, dropping to 8.3% in 2014. This implies the national poverty line is set higher than the World Bank’s extreme poverty line. The poverty gap followed similar trends, declining from 3.0% in 2007 to 1.8% in 2017. The latter figure is only slightly down from 1.9% in 2012. Indeed, the poverty gap increased in rural areas between 2012 and 2017, while it decreased in urban areas (Figure 1.2).

Figure 1.2. Poverty fell in rural and urban areas in 2018
Urban, rural and national poverty rates (2007, 2012, 2017, 2018)
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Source: Statistics Indonesia (2019[3]), Poverty and Inequality: Basic Statistics, bps.go.id/subject/23/kemiskinan-dan-ketimpangan.html#subjekViewTab3.

Box 1.2. The poor population is relatively homogenous

Organisation for Economic Co-operation and Development (OECD) experts conducted a Latent Class Analysis (LCA) for this report, based on the 2016 Survei Sosial Ekonomi Nasional (SUSENAS; National Socio-Economic Survey), to understand better the profile of poor households (Statistics Indonesia, 2016[4]). This technique clusters poor households based on several characteristics, including household composition (presence of children or the elderly), employment status of the household head (in particular, informal employment) and extreme poverty (based on the food poverty line). Additional characteristics are then evaluated across groups, including more detailed household composition, household head gender and age, and household location. Chapter 3 discusses receipt of social protection benefits.

Figure 1.3 displays household clusters according to socio-economic and demographic composition. There is notable homogeneity among clusters, which (in theory) makes it easier to design social protection policies to alleviate poverty. The two largest clusters, 1 and 2, account for 68% of the poor population and have very similar characteristics, with the exception that households in cluster 2 are slightly larger, and a significantly higher proportion of them fall under the food poverty line (40% vs. 25% in cluster 1). As discussed in Chapter 5, these two clusters also differ according to their access to social protection.

The presence of children in a household is a common feature across poor households. Households in all clusters except cluster 4 contain children under age 16, with the number of children under age 5 particularly high in clusters 1, 2 and 3. These clusters represent large households without elderly individuals (defined as over age 60) in which the dependency ratio between children and working-age individuals is about 1. Cluster 3 households differ due to the formal employment of household heads vs. their informal employment in clusters 1 and 2.

Households in cluster 4 tend to be the smallest (2.8 individuals on average) and typically lack children. The household head is often elderly, one-third of them lack working-age individuals, and some 20% of household heads are female, who often show low educational attainment relative to household heads in other clusters.

Households in cluster 5 typically contain both elderly members and children. As a result, they have the highest dependency ratio: 44% have a ratio of two dependants to one working-age individual.

Figure 1.3. Indonesia’s poor population is relatively homogenous
LCA clusters for Indonesia’s poor population (2016)
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Note: DR = dependency ratio. Extreme poor = food poor. Informal/formal HHH = Informally/formally employed household head. U5 = Under age 5.

Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

A large proportion of the population is vulnerable to falling into poverty in the event of a household-level or macroeconomic shock (Figure 1.4). Between 2011 and 2016, the proportion of the population with an income between the poverty line and 1.2 times the poverty line (classified as near poor) fluctuated within a narrow band, averaging 10.9%. The proportion with incomes between 1.2 times and 1.6 times the poverty line (the vulnerable poor) also fluctuated over this period, averaging 18.9%. Overall, the proportion classified as poor or vulnerable fell from 42.6% to 38.2% between 2011 and 2016.

Figure 1.4. Around 40% of the population is poor or vulnerable
Disaggregation of the population by poverty status (2011-16)
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Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Poverty status in Indonesia is dynamic. Although large numbers of individuals have emerged from poverty, a smaller (but still significant) number have moved in the other direction. According to data from the Indonesia Family Life Survey (IFLS), shown in Figure 1.5, 75.8% of the population that was poor in 2007 had emerged from poverty by 2014. Meanwhile 63.7% of the population with an income between the poverty line and 1.5 times the poverty line (the definition of vulnerable typically used by the Social Protection System Review) were neither poor nor vulnerable by 2014 (OECD, 2018[8]). However, 4.8% of individuals who were neither poor nor vulnerable fell below the poverty line in 2014, while 12.9% of those classified as vulnerable did likewise.

While poverty has fallen over the past two decades, inequality has risen. The Gini coefficient increased from 0.30 points in 2004 to 0.41 points in 2014, one of the fastest increases in the region. Estimates based on the 2016 SUSENAS put the Gini coefficient at 0.39 nationally, up slightly from 2010 but down from 2013. Inequality is higher in urban than in rural areas (Figure 1.6).

Inequality has been on a downwards trajectory since 2017. According to the BPS, the national Gini coefficient was 0.384 in September 2018, just below the GoI’s target for the year, of 0.385. BPS publishes inequality data twice a year, at the same as it publishes poverty data.

Figure 1.5. Many individuals have emerged from poverty but some are becoming poor
Transitions in and out of poverty between 2007 and 2014
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Source: RAND Institute (2007[9]; 2015[10]), Indonesian Family Life Survey, www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html.

Figure 1.6. Urban inequality is higher than rural
Urban, rural and national Gini coefficients (2010, 2013, 2016, 2018)
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Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Growth incidence curves show that the poor have not benefited as much as other income groups from the growth of the economy since 2010 (Figure 1.7). Economic growth between 2010 and 2013 primarily benefited households in the top quintile. The lowest decile also benefited more than average, but this was associated with the weakest consumption increases for households in the third to the seventh deciles. This trend was reversed in 2013-16: consumption increased by the lowest amount for the bottom and top three deciles. Individuals in the middle of the income distribution benefited more than other groups (TNP2K, 2018[11]).

The middle of the income distribution is not analogous to a middle class. According to the World Bank, Indonesia’s middle class has grown significantly, from 7% of the population in 2002 to 20% in 2016, but this is still low by regional standards. Indonesia’s National Team for the Acceleration of Poverty Reduction (Tim Nasional Percepatan Penanggulangan Kemiskinan, TNP2K) has calculated the middle class to be smaller still, with only the top 13.6% of the income distribution qualifying as secure middle class and rich in 2017 (TNP2K, 2018[11]). Expanding the middle class is critical to the country’s long-term prosperity, both as a driver of consumption growth and by expanding the tax base, thereby improving low levels of domestic resource mobilisation (World Bank, 2018[12]).

Figure 1.7. Recent growth has benefited individuals in the middle of the income distribution
Growth incidence curves (2010-13, 2013-16)
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Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Broader measures of deprivation, or multi-dimensional poverty, affect a higher proportion of Indonesia’s population than monetary poverty, indicating gaps in provision of basic services such as health, education and infrastructure. According to Oxford Policy and Human Development Initiative’s Multidimensional Poverty Index (MPI), the multi-dimensional poverty rate fell from 20.8% in 2007 to 15.5% in 2012. However, the intensity of deprivation remained high over this period (Figure 1.8).

Figure 1.8. Prevalence and intensity of multi-dimensional poverty are declining slowly
Level and intensity of multidimensional poverty (2007, 2012)
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Source: OPHI (2017[13]), “OPHI Country Briefing 2017: Indonesia”, Oxford Poverty and Human Development Initiative (OPHI), Oxford.

A decomposition of the MPI shows that child mortality was the dimension with the highest prevalence in 2012, affecting 12.1% of the population, down from 14.4% in 2007. Lack of access to improved sanitation and drinking water were also relatively prevalent factors contributing to the MPI rate, although in both cases, the proportion of the population deprived in these areas declined significantly between 2007 and 2012 (Figure 1.9).

Figure 1.9. Child mortality is among the leading dimensions of deprivation
Factors of deprivation (2007, 2012)
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Source: OPHI (2017[13]), “OPHI Country Briefing 2017: Indonesia”, Oxford Poverty and Human Development Initiative (OPHI), Oxford.

Indonesia’s score on the Human Development Index (HDI) has increased steadily in recent decades (up 30.5% from 1990 to 0.689 in 2015). Life expectancy at birth rose from 63.3 years in 1990 to 69.1 years in 2015, while mean years of schooling more than doubled, from 3.3 years to 7.9 years, and gross national income per capita increased by 135.4% to USD 10 053. Yet, its 2015 score was below the East Asia and the Pacific average, placing Indonesia 113th out of 188 countries and territories included (UNDP, 2016[14]).

Regional differences

There is a strong spatial dimension to poverty and inequality in Indonesia. Differences exist both between urban and rural areas and between different regions; the east of the country is significantly more deprived than other areas. This phenomenon has prompted successive administrations to adopt a dual approach to poverty reduction that combines social protection with efforts to stimulate local economic development through community infrastructure initiatives financed by large-scale fiscal transfers.

In 2007, the official poverty headcount ratio in urban areas was 12.5%, compared with 20.4% in rural areas. By 2016, the poverty headcount had fallen to 7.7% in urban areas and 14.0% in rural areas. As a consequence of sustained and rapid urbanisation, the urban population exceeded the rural population in 2011, having accounted for just 30.5% in 1990. As a result, rural poverty’s weight on the national poverty rate is declining and will continue to do so: two-thirds of the population is expected to live in urban areas by 2050.

Poverty rates in eastern provinces are typically much higher than those in western or central areas, although these areas are also much less populated, which limits their weighting in national poverty figures. The five poorest provinces are in the east, and their poverty rates in 2018 were, on average, 18 percentage points higher than the average for the five least-poor provinces (Figure 1.10). The gap between Jakarta and Papua (the least-poor and poorest provinces) in 2018 was close to 25 percentage points.

Figure 1.10. Poverty rates vary greatly by province
Poverty rates in the poorest and least-poor provinces (2007, 2010, 2013, 2018)
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Note: DKI = Daerah Khusus Ibukota.

Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

A 2014 reform to increase the autonomy and the resources available for villages’ economic and social development is a critical component of the GoI’s current efforts to address regional disparities (Box 1.3). The so-called Village Funds represent an important mechanism for reducing poverty that is not considered as social protection but which nonetheless has numerous potential overlaps with social protection.

Box 1.3. Reaching marginalised areas and people through the Village Funds

In 2014, the GoI enacted a new Village Law (Law No. 6/2014), which significantly increased the autonomy and financial resources of Indonesia’s 74 091 villages. This reform of the country’s decentralised system of governance is intended to promote economic development in areas that have so far not benefited from Indonesia’s economic growth to the same extent and to promote the inclusion of marginalised individuals and groups. The reform therefore tackles inequality at regional and individual levels.

Some 89% of Village Funds expenditure was allocated to infrastructure development in 2015, the first year of operation. Around 6% of spending was allocated to building construction and 2.5% to capacity building and empowerment, respectively. Lack of infrastructure in many rural areas is a key constraint on communities’ livelihoods and welfare. Infrastructure was a priority of existing village development plans, which, to a large extent, drove allocation of the funds in the early years. There is an emphasis on road construction, but projects to improve drainage and sanitation have also been implemented.

The full economic impact of the Village Funds will take time to emerge as new and better roads gradually enhance villages’ access to markets and other opportunities. The evaluation shows that villages are as keen to generate non-agricultural activities as they are to boost the productivity of local farmers; funds are invested in village-owned enterprises, and individuals (especially women) are provided with training in a range of skills considered valuable to the local economy.

At present, the direct economic benefits – largely in the form of participant wages and purchase of building materials – tend to stay in the villages, but the meso-level benefits associated with greater connection among villages could be significant. As these emerge, it will be important that national infrastructure, rural development and labour policies are cognisant of, and aligned with, the impact of Village Funds (Gama, Saget and Elsheikhi, 2018[15]).

The GoI is currently undertaking efforts to ensure Village Funds benefit the poorest individuals. At a national level, it has revised the formula by which resources are allocated to ensure that the least developed villages receive proportionally greater funding. At the same time, it is pushing projects to rebalance towards building basic infrastructure and economic empowerment of disadvantaged groups.

Women, children and the elderly are most exposed to risks across the life cycle

Indonesians face clear risks at different points in the lifecycle and are especially vulnerable in early childhood and old age. Women are poorer at almost all ages than men and the disadvantages they face relative to men are compounded over the course of the lifecycle. For the majority of Indonesia’s working-age population, informal employment is a critical source of income but is also associated with heightened poverty and vulnerability.

This section analyses the different risks confronting different age groups with a view to understanding where greatest demand for different types of social protection intervention might be required. It starts by examining the age structure of Indonesia’s population and how this is expected to change over the 21st Century, thereby providing insights into future dynamics of demand for social protection,

Age profile

Poverty in Indonesia has a clear age profile (Figure 1.11). Poverty rates are highest amongst children (especially the very young) and amongst the elderly. Although poverty rates declined across all age groups between 2010 and 2016, the decline was negligible for individuals aged over 65, an age group which currently has little access either to social assistance or social insurance. Recent analysis by TNP2K demonstrates that poverty is higher amongst women than men at nearly every age (TNP2K, 2018[11]).

Figure 1.11. Poverty is concentrated among children and the elderly
Poverty by five-year age cohort, 2010 and 2016
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Source: Statistics Indonesia (2016[4]; 2010[16]), Survei Sosial Ekonomi Nasional (SUSENAS), https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Indonesia experienced a rapid demographic transition in the last 65 years (Figure 1.12). Birth rates increased rapidly following independence in 1945, resulting in a baby boom evident in the size of today’s working-age population. Birth rates have since declined, associated in part with a successful birth control programme introduced in the 1970s. The total fertility rate declined from 5.7 children per woman in the early 1960s to 2.4 in 2015. Mortality more than halved over the same period.

Figure 1.12. Indonesia has made a rapid demographic transition
Population growth and rate of birth and deaths in Indonesia (1950-2015)
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Source: UNDESA (2017[17]), World Population Prospects: The 2017 Revision, https://www.un.org/en/development/desa/population/publications.

The large size of the working-age population and the declining fertility rate are driving a decline in Indonesia’s dependency ratio, which measures the number of children up to age 15 and adults over age 65 as a proportion of the population between these ages. Elsewhere in Asia, declines in the dependency ratio have been a key driver of economic growth (Bloom and Williamson, 1998[18]).

Indonesia’s dependency ratio declined from 67.3 to 49.2 between 1990 and 2015 and is projected to keep falling until it reaches 46.4 in 2030 (UNDESA, 2017[17]). However, even at the present time, the ageing of the baby boom generation is causing a rapid increase in the old-age dependency ratio. From 2030, this will offset a continued decline in the child dependency ratio to reverse the decline in the total dependency ratio.

The child dependency ratio was 41.6 in 2015 and is projected to decline to 29.2 by 2055. By contrast, the old-age dependency ratio was 7.6 in 2015 and will nearly triple to 22.4 by 2055. As the population ages, the proportion age 24 or under will fall, from 45% in 2015 to 33% in 2055 (Figure 1.13). In 2015, 20% of the population was younger than age 10, a proportion projected to fall to 12% in 2055.

Figure 1.13. The old-age dependency ratio is about to start a rapid ascent
Indonesia’s child, old age and total dependency ratios (1950-2100)
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Note: Dependency ratio is defined as the proportion of the child and/or old population as a proportion of the working age population (age 15-65).

Source: UNDESA (2017[17]), World Population Prospects: The 2017 Revision, https://www.un.org/en/development/desa/population/publications.

Changes in the age structure of the population will affect demand for public services, including social protection. The current focus on alleviating child poverty and promoting early childhood development will shift over time to programmes for older people, who are currently reliant on family members for income support in the absence of a large-scale public pension arrangement. Increasing coverage of contributory pensions is a policy priority of the GoI but this objective will be difficult to achieve, especially for individuals closer to retirement today.

Maternal mortality

Indicators related to the early lifecycle stages have improved significantly since 2000, but more progress is needed to achieve parity with other countries in South-East Asia. The potential of social protection to increase beneficiaries’ access to basic services, in particular health and education, gives it a critical role in further enhancing these outcomes.

Indonesia’s maternal mortality ratio declined from 265 to 126 deaths per 100 0000 live births between 2000 and 2015 (Figure 1.14). Although a significant reduction, it fell short of the 2015 target set by the United Nations Millennium Development Goals (MDGs), and the indicator remains high relative to Malaysia, the Philippines and Thailand, although convergence is evident.

Figure 1.14. Maternal mortality has more than halved since 2000
Maternal mortality across benchmark countries (2000, 2005, 2010, 2015)
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Source: World Bank (2018[5]), World Development Indicators (database), data.worldbank.org/products/wdi.

The decline in maternal mortality is partly attributable to the increased proportion of women accessing antenatal care: access has steadily increased since the early 1990s, from 76.3% in 1991 to 95.5% in 2013 (Figure 1.15A). However, antenatal care is correlated with income and education (Figure 1.15B): 99.0% of pregnant higher education graduates received antenatal care vs. 76.6% of those with no education (MoH, 2013[19]).

Figure 1.15. Access to medical services before and at birth is close to universal
Access to ante-natal care (1991-2013) and proportion of births attended by skilled medical personnel (2013)
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Source: World Bank (2018[5]), World Development Indicators (database), data.worldbank.org/products/wdi; MoH (2013[19]), Basic Health Research, Ministry of Health, Government of Indonesia.

Infant and child mortality

Indonesia’s infant mortality ratio has also fallen significantly, from 40.5 to 23.0 deaths of children under age 1 per 1 000 live births between 2000 and 2015 (Figure 1.16A). As with maternal mortality, this indicator is higher in Indonesia than in the other benchmark countries but the gap has decreased significantly.

Indonesia achieved more significant reduction in child mortality, which declined from 81.9 to 27.8 deaths of children under age 5 per 1 000 between 1990 and 2015 (Figure 1.16B). This 66% decrease means Indonesia achieved MDG target of reducing child mortality by two-thirds. It has also reduced this indicator faster than neighbouring countries. As part of its agenda for achieving the succeeding United Nations Sustainable Development Goals, the GoI aims to reduce child mortality to 25 deaths per 1 000 children by 2030.

Figure 1.16. Mortality under age 5 has fallen since the 1990s
Mortality rates among children under age 1 and mortality under age 5 against benchmark countries (1990-2015)
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Source: World Bank (2018[5]), World Development Indicators (database), data.worldbank.org/products/wdi.

Vaccination rates have increased significantly in the past two decades but fall far short of 100%. Tuberculosis and polio vaccinations are most common, with 90% of children under age 5 immunised. Rates are lower for measles and diphtheria, pertussis and tetanus. The proportion of children under age 5 fully immunised is 60.0%: 56.7% in rural areas and 63.2% in urban areas. Aceh and Papua have the lowest immunisation rates, with 40.4% of children under age 5 fully immunised in 2016 (Statistics Indonesia, 2016[20]).

Stunting, or low height-for-age, affects a significant proportion of children, although the prevalence has declined recently.1 In 2018, 30.8% of children under age 5 suffered from stunting, down from 37.2% in 2013. Between 2007 and 2013, the stunting rate had increased (Figure 1.17B) (Statistics Indonesia, 2016[20]). Further declines in stunting can be expected after the GoI launched the National Strategy to Accelerate Stunting Prevention (StraNas Stunting) 2017-2021, which is based on a multi-sectoral approach (Rokx, Subandoro and Gallagher, 2018[21]).

Ministry of Health (MoH) data indicate that the prevalence of stunting is higher amongst poor households, with a 10.8 percentage point gap between households in the bottom and top wealth quintiles in rates of severe stunting (MoH, 2013[19]) (Figure 1.17A). A recent World Bank study showed that 49% of children under age 5 in the lowest two quintiles were stunted, up from 43% in 2007 (World Bank, 2017[22]). The stunting rate also varied by province in 2018, from 17.7% in DKI Jakarta to 42.6% in Nusa Tenggara Timur.

Figure 1.17. Stunting remains a significant problem
Stunting levels of children by income group and over time (2007, 2010 and 2013)
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Source: UNESCO (2017[23]), UIS.Stat (database), data.uis.unesco.org.

Stunting in the first two years, part of the “critical window of opportunity” spanning the first 1 000 days from conception, can lead to long-term adverse effects on an individual’s physical and cognitive development. Undernourished children are more likely to have reduced adult height, decreased school attendance, lower wages in adulthood and give birth to smaller infants. Considerable evidence also shows a strong correlation between stunting and lowered cognitive ability (Victora et al., 2008[24]; Grantham-McGregor et al., 2007[25]; Hoddinott et al., 2013[26]).

Access to education at an early age is an important means of promoting cognitive development. Although Indonesia has made strides in improving access to pre-primary education in recent years, further progress is needed, especially in rural areas. UNESCO data indicate that the gross enrolment ratio for pre-primary education more than doubled in a decade, from 27.0% in 2004 to 58.2% in 2014 (Figure 1.18). However, once again, access improves with income: according to Jung and Hasan (2016[27]), a four-year-old from the poorest quintile has a 16% likelihood of accessing early childhood education and development services versus 40% from the richest 20% of households (2016[27]).

Figure 1.18. Enrolment in pre-school education has increased
Pre-school enrolment by sex (2000-14)
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Source: UNESCO (2017[23]), UIS.Stat (database), data.uis.unesco.org.

Despite high rates of stunting and low enrolment of children from low-income households, there is evidence that public interventions are succeeding in addressing these challenges successes. The Posyandu village health facilities provided an integrated health and nutrition programme in the 1990s, while more recently, a community-driven development programme, Generasi, contributed to reducing rates of stunting.

Social protection is a critical policy component in improving developmental outcomes, thus enhancing the likelihood of escaping poverty. The Pendidikan Anak Usia Dini (Early Childhood Education and Development) programme has driven improvements in both pre-school enrolment and duration of enrolment of poor children in implementing communities. Program Keluarga Harapan (PKH; Family of Hope Programme), the GoI’s conditional cash transfer discussed in Chapter 2, is associated with reduced severe stunting, higher school enrolment and improved use of health care services.

The GoI has the instruments to address persistently high rates of stunting and continued wealth-based inequalities in access to health, nutrition and education services more broadly. However, as the World Bank (2017[22]) states, these programmes are “neither integrated nor implemented at scale” in Indonesia.

Education

In 2016, Indonesia increased the duration of compulsory education from 9 to 12 years to maximise the long-term economic potential of its large youth population. The change is likely to increase the number of children who complete high school and demand for tertiary education. The reform will impose significant capacity and financial burdens on the state, since it is obliged to meet basic education costs. The GoI currently provides scholarships to poor children to encourage attendance (see Chapter 2); coverage of this intervention will need to match growth in enrolment if children from lower-income groups are to benefit.

Elementary (primary) school and junior high (lower secondary) school are free, and coverage is almost universal (Figure 1.19). Junior high enrolment has increased significantly in recent years, from 85.5% in 2009 to 94.7% in 2015. Rural enrolment remains lower than urban enrolment but the gap shrunk, from 8.0 percentage points in 2009 to 3.2 in 2015. Female enrolment is slightly higher than for males for both elementary and junior high school.

Figure 1.19. School enrolment is high and rising
Enrolment in elementary and junior high school, by region and sex (2009-15)
picture

Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

The gross enrolment rate is much lower for senior high (upper secondary) school, which was fee-paying until 2016. Enrolment increased by 15.4 percentage points to 70.6% between 2009 and 2015 (Figure 1.20) and will increase significantly with 12-year compulsory schooling. Again, females are slightly more likely to be enrolled than males. Until recently, less than one-third of students graduated (Shin Jongsoon et al., 2017[28]).

Student outcomes are not improving at the same pace as enrolment, contributing to skills shortages in the economy (Allen, 2016[29]). The latest results of the OECD Programme for International Student Assessment, in which the country has participated since 2000, showed that around three-quarters of 15 year-olds do not have basic skills in mathematics and less than one-third have basic reading proficiency (OECD, 2018[30]). However, Indonesian students showed one of the strongest improvements in science assessments among participating countries between 2012 and 2015 while their performance in mathematics assessments also improved also over the period (OECD, 2016[31]).

Figure 1.20. Senior high school enrolment is rising rapidly
Enrolment in senior high school by region and sex (2009-15)
picture

Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Tertiary education enrolment almost doubled between 2000 and 2016, from 14.9% to 27.9%, although it remains below enrolment in the benchmark countries (Figure 1.21). Growth in the proportion of students completing secondary education drove demand for tertiary education even before the expansion in basic education (Negara and Benveniste, 2014[32]). Evidence indicates there are significant returns to higher education in terms of labour force participation, wages and job availability (Negara and Benveniste, 2014[33]).

Only 10% of students enrolled in tertiary education were from the lowest two income quintiles in 2012, despite legislation requiring that 20% of higher education students come from “frontier, outer and disadvantaged areas” (Negara and Benveniste, 2014[32]; Sekretariat Negara Republik Indonesia, 2012[34]). This disparity in education outcomes predicated on income risks exacerbating and perpetuating inequalities.

Figure 1.21. Tertiary enrolment lags behind benchmark countries
Gross enrolment ratios in tertiary education across benchmark countries (2000-16)
picture

Source: World Bank (2018[5]), World Development Indicators (database), data.worldbank.org/products/wdi.

Increased enrolment in senior high school and tertiary education is associated with a decline in the proportion of youth (aged 15-24) not in education, employment or training (NEET) (Figure 1.22). Some 23.2% of Indonesian youth were classified as NEET in 2016, a 6.4 percentage point decrease from 2008 (ILO, 2015[35]). The percentage of female youth NEET is considerably higher than for males, although the gap appears to be shrinking.

Figure 1.22. The proportion of youth NEET remains high, especially among women
Proportion of males and females NEET (2008-16)
picture

Source: ILO (2015[35]), Key Indicators of the Labour Market, 9th edition (database), www.ilo.org/global/statistics-and-databases/research-and-databases/kilm/lang--en/index.htm (accessed 22 June 2018).

Adulthood

Although the labour force has grown significantly in absolute terms, rates of labour force participation have changed little since 2000. There is a major disparity in labour market participation between men and women, contributing to women’s vulnerability at working age as well as in old age. The average labour force participation rates for men and women between 2000 and 2015 were 79.7% and 47.1% respectively and there is little evidence of convergence (Figure 1.23). The gender pay gap for full-time workers in 2014 exceeded 30% (OECD, 2017[36]).

Figure 1.23. A large gender gap exists in labour force participation
Male and female labour force participation (2000-17)
picture

Source: ILO (2015[35]), Key Indicators of the Labour Market, 9th edition (database), www.ilo.org/global/statistics-and-databases/research-and-databases/kilm/lang--en/index.htm (accessed 22 June 2018).

The economy has so far been able to accommodate strong growth in the labour force. The national unemployment rate fell from 10.3% in 2006 to 5.6% in 2016. Although rates vary by province, they declined in all provinces except Kalimantan Utara between 2006 and 2016 (Pratomo, 2015[37]). Although unemployment is low, approximately 30% of the labour force is thought to be underemployed and workers also confront widespread low pay and long working hours (OECD, 2017[36]).

Figure 1.24. Unemployment has declined and varies by province
Unemployment rate by province, 2006 and 2016
picture

Note: DI = Daerah Istimewa; DKI = Daerah Khusus Ibukota.

Source: Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

The rate of informality has declined but nonetheless accounts for the majority of employment in Indonesia. Between 2006 and 2016, the rate of informal workers (defined by Statistics Indonesia as own-account workers, temporary or casual employees and unpaid family workers) fell from 68.9% to 57.6% (Figure 1.25A). Informality is associated with elevated exposure to a range of risks. Defining informality at the household rather than individual level, this review finds that households containing informal workers tend to be worse off.

According to IFLS data for 2014, 42.9% of the informal workforce were own-account workers, 42.4% were employees, 12.5% were unpaid family workers and 2.2.% were employers. Men were more likely to be informal employees than women (47.8% versus 35.4%) while women were much more likely to be unpaid family workers than men (21.6% versus 5.4%).

Figure 1.25. Informality is declining slowly
Informality rate over time (2006-16) and by level of education (2016)
picture

Note: National definitions used in the estimation of informality.

Source: Authors’ calculations based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

The likelihood of being informally employed decreases with higher educational attainment: 35.3% of individuals who only completed elementary education are in informal work compared with 1.9% of university graduates (Figure 1.25B).Based on data from the IFLS for 2014/15, informality is inversely correlated with income. In the first quintile, 86% of households contain only informal workers compared with 57% in the top quintile (Figure 1.26A). Some 91% of households classified as poor by the global USD 1.90/day benchmark contain only informal workers (Figure 1.26B).

Figure 1.26. Informality and poverty are correlated
picture

Source: Authors’ calculations, based on RAND Institute (2015[10]), Indonesian Family Life Survey – Wave 5, www.rand.org/labor/FLS/IFLS.html.

As discussed in Chapter 3, informal households are less likely to be covered by social protection, which in turn increases their exposure to health and other shocks. The GoI is making a particular effort to expand contributory arrangements in the informal sector. Informal workers often find themselves in the “missing middle” of social protection coverage, whereby they are ineligible for poverty-targeted social assistance but do not have access to employer-based social insurance arrangements, making it important that they can access public schemes.

Old age

Indonesia’s elderly population is relatively small but it is highly prone to poverty. In 2016, 14.7% of individuals over age 65 were poor, down only slightly from 14.9% in 2010. Poverty among all other age groups declined over the same period, most significantly among children, the cohort in which poverty was previously most concentrated (Figure 1.27B).

Figure 1.27. Old-age poverty has barely declined in recent years
Main activity of elderly population (2016) and poverty rate by age (2010 and 2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

The majority of elderly individuals do not receive support from public social protection. Contributory pension arrangements have historically been targeted at formal workers, resulting in less than 10% coverage among today’s working population and lower still among the elderly (OECD, 2017[36]). Social assistance has been extended to elderly individuals but coverage remains extremely low (see Chapter 2).

In 2007, only 6.8% of individuals aged 60-64 received a pension. The rate decreased for older cohorts: coverage was 4.9% for ages 70 to 74 and 2.7% for age 75+. Women were three to five times less likely to receive a pension (Priebe and Howell, 2014[38]). Elderly individuals, especially women, are highly reliant on transfers from household members.

Nearly one in three men continue to work past age 60 (Figure 1.27A). However, the disability rate among the population aged over 65 has been calculated to be 46.5%, much higher than for the population as a whole. This limits the elderly’s population remain economically active and support themselves (Adioetomo, Mont and Irwanto, 2014[39]). With the elderly population starting to increase rapidly, the need to expand old-age social protection will become increasingly pressing; failure to do so would risk driving poverty rates upwards

Health risks

Indonesia is undergoing an epidemiological transition. Non-communicable diseases are becoming a more significant burden on health than communicable diseases: the proportion of total deaths attributable to non-communicable diseases rose from 63% in 2010 to 77% in 2014. The most common causes of death in 2014 were cardiovascular diseases, cancer and diabetes (WHO, 2014[40]).

This transition is borne out by the number of Disability-Adjusted Life Years (DALYs) lost to major causes from 2000 to 2015 (Table 1.1). Although communicable diseases still accounted for the largest proportion of DALYs in 2015, their contribution declined by 32.5% from 2000. There were significant increases for cardiovascular diseases; diabetes; and urogenital, blood, endocrine and chronic liver diseases.

Table 1.1. The health burden of non-communicable diseases is increasing
Changes in principal causes of loss of DALYs (2000-15)

Cause

2000

2005

2010

2015

Percentage point change (2000-15)

Communicable, maternal, neonatal and nutritional diseases

29 556

26 231

23 211

20 005

-32.3

HIV/AIDS and tuberculosis

4 051

4 114

4 172

4 156

2.6

Diarrhea, lower respiratory and other common infectious diseases

12 873

10 232

8 150

6 575

-48.9

Neglected tropical diseases and malaria

1 617

1 648

1 536

1 549

-4.2

Maternal disorders

906

754

597

511

-43.6

Neonatal disorders

7 474

6 899

6 260

4 974

-33.5

Nutritional deficiencies

2 075

2 036

1 979

1 765

-15.0

Other communicable, maternal, neonatal and nutritional diseases

558

547

516

475

-14.9

Neoplasms

3 199

3 576

4 023

4 516

41.2

Cardiovascular diseases

10 216

11 280

12 400

13 769

34.8

Chronic respiratory diseases

3 149

3 209

3 365

3 225

2.4

Cirrhosis and other chronic liver diseases

1 465

1 587

1 743

1 738

18.6

Digestive diseases

1 824

1 897

1 881

2 110

15.7

Neurological disorders

1 851

2 004

2 165

2 318

25.3

Mental and substance use disorders

3 641

3 954

4 289

4 638

27.4

Diabetes and urogenital, blood and endocrine diseases

4 540

5 098

5 865

6 617

45.7

Musculoskeletal disorders

3 238

3 617

4 061

4 545

40.3

Other non-communicable diseases

4 890

5 295

5 649

6 020

23.1

Transport injuries

2 900

2 759

2 728

2 717

-6.3

Self-harm and interpersonal violence

422

457

498

538

27.6

Forces of nature, war and legal intervention

117

208

201

114

-2.3

All causes

73 498

73 634

74 537

75 269

2.4

Source: IHME (2016[41]), Global Burden of Disease (database), www.healthdata.org/gbd/data (accessed 22 June 2018).

The increase in HIV/AIDS is noteworthy. Data from the Joint United Nations Programme on HIV and AIDS indicate that nearly 690 000 Indonesians aged 15-49 lived with HIV in 2015, equivalent to a prevalence rate of 0.5%, up from 0.1% in 2003 (WHO, 2016[42]). National rates mask important regional variation. MoH data indicate that Jakarta has the largest population living with HIV/AIDS. Bali, East Java and Papua also report relatively large populations (Indonesian National AIDS Commission, 2014[43]).

Rates might be much higher than is reported, as cases often go undiagnosed. Only around 35% of individuals living with HIV/AIDS know their status (Indonesian National AIDS Commission, 2014[43]). HIV/AIDS was responsible for 35 000 deaths in 2015, leaving 110 000 children orphaned (Indonesian National AIDS Commission, 2014[43]). Few individuals diagnosed with HIV receive antiretroviral therapy: 13% received treatment in 2016 (WHO, 2016[42]).

The proportion of Indonesians who are obese has risen dramatically in recent years and represents a significant long-term health threat. The proportion of the population that is obese more than doubled between 2007 and 2018, from 10.5% to 21.8% (Ministry of Health, 2018[44]).

The high prevalence of smoking, especially among men, is a particular health concern. It is a major cause of non-communicable disease. In 2013, some 66.0% of adult males and 6.7% of females smoked, up from 62.2% and 1.3% in 2001 (MoH, 2013[45]). As of 2018, the mean age at initiation of daily smoking is 17.6 years, and 34.8% of the population aged 15 and older smoke tobacco regularly: roughly 67% of Indonesian men and 2.7% of Indonesian women (WHO Regional Office for South-East Asia, 2018[46]).

A majority of Indonesians suffering a health issue choose self-treatment instead of visiting health facilities or medical professionals, although this trend is decreasing: in 2014, 61% treated themselves, compared with 68.4% in 2009. Overall, men and rural residents are more likely to choose self-treatment, possibly because of lack of access to adequate health facilities (Figure 1.28).

Figure 1.28. The proportion of people treating themselves has declined
Percentage choosing self-treatment by location and sex (2009-14)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

People with disabilities

The 2015 Intercensal Population Survey indicated that around 8.6% of the population over age 2 have some type of disability and half (48.5%) report multiple disabilities. Using a combination of population and household surveys, TNP2K has calculated the rate to be between 10% and 15% of the population but suggests that the actual rate could be much higher. Rates are higher for females than for males and for rural residents than for urban residents and individuals with disabilities face more risks and vulnerabilities than the general population (Adioetomo, Mont and Irwanto, 2014[39]).

Poverty rates among households with a member with a disability are 2.4 percentage points higher than among those without. Individuals with disabilities are less likely to be enrolled in school, and those with a disability have a 66.8% probability of completing primary education relative to those with no impairments. Individuals with a mild disability are 31.1% less likely to be employed than un-impaired individuals; those with severe impairments are 91.8% less likely.

A legislative framework for integrating people with disabilities within mainstream development policies is being established. Following the ratification of the United Nations Convention on the Rights of Persons with Disabilities in 2007 and its passing into law in 2011, Indonesia passed Law No. 8/2016 on Persons with Disabilities in 2016 that takes a rights-based approach to disability (Wardana and Dewi, 2017[47]). Improving access to social protection for people with disabilities is a critical enabler for their integration within the developmental process. According to TNP2K, just 1% of people with disabilities access Asistensi Sosial Orang Dengan Kecacatan Berat (ASODKB), the principal social assistance programme for this group. ASODKB, which became Asistensi Sosial Penyandang Disabilitas Berat, is discussed further in Chapter 2.

Climate change is a threat while Industry 4.0 poses opportunities and risks

Indonesia’s long-term economic performance will be a key determinant of demand for social protection in the future. The prospects for sustained economic growth to drive increases in per capita income and improvements in living standards are positive. However, the associated impact on poverty and inequality will depend on the structure of this growth and the extent to which it can be harnessed by the tax system to finance redistributive fiscal policies. At the same time, the structure of employment will influence the extent to which Indonesia is affected by challenges such as climate change and the Fourth Industrial Revolution

Indonesia’s economic structure has changed significantly in the past 20 years, with a shift away from the primary sector. The service sector accounted for 43.6% of GDP in 2017, while industry accounted for 39.4% and agriculture for 13.1% (Figure 1.29). The service sector is also the largest employer, accounting for 48% of employment in 2017, up from 37% in 2000, while agriculture’s employment share fell from 45% to 30% over the same period (World Bank, 2018[5]). Employment in industry increased from 17% to 22% (World Bank, 2018[5]).

Figure 1.29. Agriculture’s share of output and employment has declined
Composition of GDP and employment (2017)
picture

Source: World Bank (2018[5]), World Development Indicators, accessed 21 January 2019.

These economic and labour-market changes have been important drivers of enhanced productivity and instrumental in accommodating growth in the working-age population. Indonesia’s economic prospects are positive: the economy is expected to maintain growth rates in excess of 5% up to 2020, inflation is mild, and monetary and fiscal policy are contributing to a stable macroeconomic environment. However, Indonesia risks being caught in a middle-income trap if it doesn’t generate more inclusive growth and strengthen human capital.

Indonesia is approaching 40 years as a middle-income country, which is significantly longer than some of the East Asian tigers, such as South Korea (Estrada et al., 2017[48]). Neighbours such as the Philippines and Malaysia are expected to graduate to high-income status before Indonesia. Weaknesses in public service delivery through the decentralised system of government has been identified as a key constraint to Indonesia’s graduation (Huang, Morgan and Yoshino, 2018[49]).

Climate change is likely to have an adverse impact on the economy and labour force over the longer term. Indonesia was the fifth-largest emitter of greenhouse gases in the world in 2017 but has pledged to reduce emissions by 29% from a business as usual scenario by 2030 (World Resources Institute, 2016[50]). A 2011 International Food Policy Research Institute (IFPRI) study shows that climate change will reduce GDP between now and 2030. Output will also decrease for some crops, such as rice and paddy, increasing their prices and reducing food security (Oktaviani et al., 2011[51]).

At the same time, the proportion of the population vulnerable to climate change will increase significantly. An Asian Development Bank study indicates that the population vulnerable to poverty will increase by between 15% and 91% due to the increased flooding and drought associated with climate change (Fujii, 2016[52]). Non-climate related natural disasters also pose a major threat to livelihoods: Indonesia is considered extremely vulnerable to earthquakes and tsunamis, as demonstrated by the events that struck Sulawesi in September 2018 and Sunda Strait in December 2018.

According to the National Disaster Mitigation Agency, there were 2 426 natural disasters in Indonesia in 2018, which left 4 231 people dead or missing (Heriyanto and Cahya, 2018[53]). Social protection has been shown to be an important part of the policy response to natural disasters and climate shocks in Southeast Asia. It can support households not only to cope with shocks but also adapt to climate change and enhance resilience (Hallegatte et al., 2016[54]). The human and economic benefits of ex ante responses relative to disaster relief are substantial.

The Fourth Industrial Revolution is likely to have a profound influence on Indonesia’s future. It present great opportunities in terms of increasing productivity, higher growth rates, and better-paid jobs, as a result of which rising demand will offset declines in employment associated with automation (Asian Development Bank, 2018[55]).

However, it also threatens disruption, with some sectors more affected by automation than others. According to the International Labour Organization (ILO), 85% of salaried workers in Indonesia’s retail sector – a major source of employment for women – are at high risk from automation. At the same time, 60% of salaried workers in automotive engineering, more than 60% of salaried workers in electrics and electronics, and 64% of salaried workers in textiles, clothing and footwear are also at high risk (Chang, Rynhart and Huynh, 2016[56]).

As the Asian Development Bank notes, the policy response to Industry 4.0 must be wide-ranging: “Governments should respond to these challenges by ensuring that workers are protected from the downside of new technologies and able to harness the new opportunities they provide. This will require co-ordinated action on skills development, labour regulation, social protection, and income redistribution.” By embedding social protection in national development plans, the GoI is maximising its potential to play a supportive role both in protecting workers and enhancing long-term productivity, as well as promoting inclusive growth more broadly.

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Note

← 1. Stunting is defined as the percentage of children whose z-score for the height-for-age index is below two standard deviations below the mean.

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